Large-Area Fabrication-aware Computational Diffractive Optics

2025-05-28

AI Summary

Current differentiable optical design methods suffer from a significant performance gap between simulation and fabrication, hindering the scalable deployment of diffractive optical elements (DOEs). To address this, we propose a fabrication-aware end-to-end optimization framework. Our method introduces, for the first time, a differentiable 3D topography process model based on a super-resolution neural lithography simulator, and establishes a tensor-parallel FFT computational framework enabling joint optimization of ultra-large DOEs (>32 mm × 21 mm). The framework is compatible with grayscale photolithography and nanoimprint lithography for mass production. Experimentally fabricated centimeter-scale DOEs demonstrate high simulation-fabrication fidelity in holographic display and point-spread-function engineering. Moreover, a single-DOE imaging system achieves high image quality after Wiener filtering, substantially advancing the practical realization of learnable diffractive optical systems.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Computer Vision: Learning & Optimization for CV
• Machine Learning: Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Differentiable optics, as an emerging paradigm that jointly optimizes optics and (optional) image processing algorithms, has made innovative optical designs possible across a broad range of applications. Many of these systems utilize diffractive optical components (DOEs) for holography, PSF engineering, or wavefront shaping. Existing approaches have, however, mostly remained limited to laboratory prototypes, owing to a large quality gap between simulation and manufactured devices. We aim at lifting the fundamental technical barriers to the practical use of learned diffractive optical systems. To this end, we propose a fabrication-aware design pipeline for diffractive optics fabricated by direct-write grayscale lithography followed by nano-imprinting replication, which is directly suited for inexpensive mass production of large area designs. We propose a super-resolved neural lithography model that can accurately predict the 3D geometry generated by the fabrication process. This model can be seamlessly integrated into existing differentiable optics frameworks, enabling fabrication-aware, end-to-end optimization of computational optical systems. To tackle the computational challenges, we also devise tensor-parallel compute framework centered on distributing large-scale FFT computation across many GPUs. As such, we demonstrate large scale diffractive optics designs up to 32.16 mm $ imes$ 21.44 mm, simulated on grids of up to 128,640 by 85,760 feature points. We find adequate agreement between simulation and fabricated prototypes for applications such as holography and PSF engineering. We also achieve high image quality from an imaging system comprised only of a single DOE, with images processed only by a Wiener filter utilizing the simulation PSF. We believe our findings lift the fabrication limitations for real-world applications of diffractive optics and differentiable optical design.

Problem

• Bridging the quality gap between simulated and manufactured diffractive optical components
• Enabling fabrication-aware, end-to-end optimization of computational optical systems
• Scaling up diffractive optics designs for mass production and large-area applications

Innovation

• Fabrication-aware design pipeline for mass production
• Super-resolved neural lithography model for 3D prediction
• Tensor-parallel compute framework for large-scale FFT